Presentation	2003/12/1 A Method to Estimate the Learning Coefficients of Singular Learning Machines by Decomposition of Kullback Information Kenji NAGATA, Sumio WATANABE,
PDF Download Page	PDF download Page Link
Abstract(in Japanese)	(See Japanese page)
Abstract(in English)	A lot of learning machines such as neural networks, normal mixtures, Bayesian networks, and hidden Markov models are singular statistical models. Their Fisher information matrices are not positive definite, hence the conventional statistical asymptotic theory does not hold. In this paper, we propose a new method to calculate the learning coefficients by decomposing the Kullback information. The effectiveness of the proposed method is shown by experimetal results.
Keyword(in Japanese)	(See Japanese page)
Keyword(in English)	Singular Learning Machines / Kullback Information / Stochastic Complexity
Paper #	NC2003-104
Date of Issue

Conference Information
Committee	NC
Conference Date	2003/12/1(1days)
Place (in Japanese)	(See Japanese page)
Place (in English)
Topics (in Japanese)	(See Japanese page)
Topics (in English)
Chair
Vice Chair
Secretary
Assistant

Paper Information
Registration To	Neurocomputing (NC)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	A Method to Estimate the Learning Coefficients of Singular Learning Machines by Decomposition of Kullback Information
Sub Title (in English)
Keyword(1)	Singular Learning Machines
Keyword(2)	Kullback Information
Keyword(3)	Stochastic Complexity
1st Author's Name	Kenji NAGATA
1st Author's Affiliation	Department of Computer Science Tokyo Institute of Technology()
2nd Author's Name	Sumio WATANABE
2nd Author's Affiliation	PI Lab., Tokyo Institute of Technology
Date	2003/12/1
Paper #	NC2003-104
Volume (vol)	vol.103
Number (no)	490
Page	pp.pp.-
#Pages	6
Date of Issue